A Comparison of a similarity-based and a feature-based 2-D–3-D registration method for neurointerventional use

Robert Mclaughlin, J. Hipwell, D.J. Hawkes, J.A. Noble, J.V. Byrne, T.C. Cox

    Research output: Contribution to journalArticle

    52 Citations (Scopus)

    Abstract

    Two-dimensional (2-D)-to-three-dimensional (3-D) registration can improve visualization which may aid minimally invasive neurointerventions. Using clinical and phantom studies, two state-of-the-art approaches to rigid registration are compared quantitatively: an intensity-based algorithm using the gradient difference similarity measure; and an iterative closest point (ICP)-based algorithm. The gradient difference approach was found to be more accurate, with an average registration accuracy of 1.7 mm for clinical data, compared to the ICP-based algorithm with an average accuracy of 2.8 mm. In phantom studies, the ICP-based algorithm proved more reliable, but with more complicated clinical data, the gradient difference algorithm was more robust. Average computation time for the ICP-based algorithm was 20 s per registration, compared with 14 min and 50 s for the gradient difference algorithm.
    Original languageEnglish
    Pages (from-to)1058-1066
    JournalIEEE Transactions on Medical Imaging
    Volume24
    Issue number8
    DOIs
    Publication statusPublished - 2005

    Fingerprint Dive into the research topics of 'A Comparison of a similarity-based and a feature-based 2-D–3-D registration method for neurointerventional use'. Together they form a unique fingerprint.

  • Cite this